scholarly journals Sequence/structural analysis of xylem proteome emphasizes pathogenesis-related proteins, chitinases andβ-1, 3-glucanases as key players in grapevine defense againstXylella fastidiosa

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2007 ◽  
Author(s):  
Sandeep Chakraborty ◽  
Rafael Nascimento ◽  
Paulo A. Zaini ◽  
Hossein Gouran ◽  
Basuthkar J. Rao ◽  
...  
Keyword(s):  
Plant Defense ◽  
Defense Response ◽  
Protein Function ◽  
Xylem Sap ◽  
Plant Diseases ◽  
Economic Losses ◽  
Data Set ◽  
Proteomic Data ◽  
Pathogenesis Related ◽  
Protein Functions

Background.Xylella fastidiosa, the causative agent of various plant diseases including Pierce’s disease in the US, and Citrus Variegated Chlorosis in Brazil, remains a continual source of concern and economic losses, especially since almost all commercial varieties are sensitive to this Gammaproteobacteria. Differential expression of proteins in infected tissue is an established methodology to identify key elements involved in plant defense pathways.Methods. In the current work, we developed a methodology named CHURNER that emphasizes relevant protein functions from proteomic data, based on identification of proteins with similar structures that do not necessarily have sequence homology. Such clustering emphasizes protein functions which have multiple copies that are up/down-regulated, and highlights similar proteins which are differentially regulated. As a working example we present proteomic data enumerating differentially expressed proteins in xylem sap from grapevines that were infected withX. fastidiosa.Results. Analysis of this data by CHURNER highlighted pathogenesis related PR-1 proteins, reinforcing this as the foremost protein function in xylem sap involved in the grapevine defense response toX. fastidiosa.β-1, 3-glucanase, which has both anti-microbial and anti-fungal activities, is also up-regulated. Simultaneously, chitinases are found to be both up and down-regulated by CHURNER, and thus the net gain of this protein function loses its significance in the defense response.Discussion. We demonstrate how structural data can be incorporated in the pipeline of proteomic data analysis prior to making inferences on the importance of individual proteins to plant defense mechanisms. We expect CHURNER to be applicable to any proteomic data set.

scholarly journals Deep Annotation of Protein Function across Diverse Bacteria from Mutant Phenotypes

2016 ◽  
Author(s):  
Morgan N. Price ◽  
Kelly M. Wetmore ◽  
R. Jordan Waters ◽  
Mark Callaghan ◽  
Jayashree Ray ◽  
...  
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Hypothetical Proteins ◽  
Data Set ◽  
Protein Coding ◽  
Bacterial Proteins ◽  
Genome Wide ◽  
Protein Functions ◽  
Mutant Phenotypes ◽  
Related Proteins

SummaryThe function of nearly half of all protein-coding genes identified in bacterial genomes remains unknown. To systematically explore the functions of these proteins, we generated saturated transposon mutant libraries from 25 diverse bacteria and we assayed mutant phenotypes across hundreds of distinct conditions. From 3,903 genome-wide mutant fitness assays, we obtained 14.9 million gene phenotype measurements and we identified a mutant phenotype for 8,487 proteins with previously unknown functions. The majority of these hypothetical proteins (57%) had phenotypes that were either specific to a few conditions or were similar to that of another gene, thus enabling us to make informed predictions of protein function. For 1,914 of these hypothetical proteins, the functional associations are conserved across related proteins from different bacteria, which confirms that these associations are genuine. This comprehensive catalogue of experimentally-annotated protein functions also enables the targeted exploration of specific biological processes. For example, sensitivity to a DNA-damaging agent revealed 28 known families of DNA repair proteins and 11 putative novel families. Across all sequenced bacteria, 14% of proteins that lack detailed annotations have an ortholog with a functional association in our data set. Our study demonstrates the utility and scalability of high-throughput genetics for large-scale annotation of bacterial proteins and provides a vast compendium of experimentally-determined protein functions across diverse bacteria.

scholarly journals Elevation of Systemic Defense in Potato Against Alternaria Solani by a Consortium of Compatible Trichoderma Spp.

2021 ◽  
Author(s):  
Sumit Kumar ◽  
Ram Chandra ◽  
Lopamudra Behera ◽  
Chetan Keswani ◽  
Estibaliz Sansinenea
Keyword(s):  
Defense Response ◽  
Growth Promotion ◽  
Trichoderma Viride ◽  
Alternaria Solani ◽  
Antagonistic Activity ◽  
Plant Diseases ◽  
Economic Losses ◽  
Total Protein Content ◽  
Trichoderma Spp ◽  
Potato Plants

Abstract The crop loss due to phytopathogens is a serious problem affecting the entire world. To avoid economic losses due to phytopathogens synthetic chemicals have been used for years generating serious concerns about the human health and environment. Today the use of beneficial microorganisms to treat phytopathogens is gaining attention. In this way, Trichoderma spp. has been used for combating plant diseases and inducing defense response in plants. With this idea in mind, in this study we evaluate the effectiveness of Trichoderma viride and T. harzianum as single as well as in combination for elevating the defense response and growth promotion activities in potato challenged with Alternaria solani. The mycelial inhibition of A. solani by T. viride and T. harzianum was recorded and compared with control. Scanning electron microscope (SEM) observation revealed the collapsed hyphae and sunken conidia of A. solani due to antagonistic activity of T. viride and T. harzianum. Induction of defense enzymes including TPC, PAL, SOD and total protein content was increased in Trichoderma spp, treated plants as compared with pathogen inoculated plants. HPLC analysis demonstrated higher production in phenolic compounds during combined application of Trichoderma spp. treated potato plants in the response of A. solani infection. Moreover, treatment with Trichoderma spp. consortium showed significant growth promotion in potato plants comparing with the control.

scholarly journals Design to Data for mutants of β-glucosidase B from Paenibacillus polymyxa: M319C, T431I, and K337D

2019 ◽  
Author(s):  
Peishan Huang ◽  
Stephanie C. Contreras ◽  
Eliana Bloomfield ◽  
Kristine Schmitz ◽  
Augustine Arredondo ◽  
...  
Keyword(s):  
Protein Function ◽  
Kinetic Data ◽  
Prediction Accuracy ◽  
Paenibacillus Polymyxa ◽  
Computational Algorithms ◽  
Enzyme Design ◽  
Data Set ◽  
Computational Tools ◽  
Protein Functions ◽  
Novel Protein

ABSTRACTThe use of computational tools has become an increasingly popular tool for engineering protein function. While there are numerous examples of computational tools enabling the design of novel protein functions, there remains room for improvement in both prediction accuracy and success. To improve algorithms for functional and stability predictions, we have initiated the development of a data set designed to be used for training new computational algorithms for enzyme design. To date our dataset is composed of over 129 mutants with associated expression levels, kinetic data, and thermal stability for the enzyme β-glucosidase B (BglB) from Paenibacillus polymyxa. In this study, we introduced three new variants (M319C, T431I, and K337D) to our existing dataset with the goal of cultivating a larger dataset to train new design algorithms and more broadly explore structure-function relationships in BglB.

Pathogenesis Related Proteins in Plant Defense Response

2011 ◽  
pp. 379-403 ◽  
Author(s):  
J. Sudisha ◽  
R. G. Sharathchandra ◽  
K. N. Amruthesh ◽  
Arun Kumar ◽  
H. Shekar Shetty
Keyword(s):  
Plant Defense ◽  
Defense Response ◽  
Plant Defense Response ◽  
Pathogenesis Related Proteins ◽  
Pathogenesis Related ◽  
Related Proteins

scholarly journals Reanalysis of global proteomic and phosphoproteomic data identified a large number of glycopeptides

2017 ◽  
Author(s):  
Yingwei Hu ◽  
Punit Shah ◽  
David J. Clark ◽  
Minghui Ao ◽  
Hui Zhang
Keyword(s):  
Breast Cancer ◽  
Protein Function ◽  
Large Scale ◽  
Vital Role ◽  
Protein Glycosylation ◽  
Data Sets ◽  
Data Set ◽  
Breast Cancer Xenograft ◽  
Proteomic Data ◽  
Large Scale Analysis

ABSTRACTProtein glycosylation plays fundamental roles in many cellular processes, and previous reports have shown dysregulation to be associated with several human diseases, including diabetes, cancer, and neurodegenerative disorders. Despite the vital role of glycosylation for proper protein function, the analysis of glycoproteins has been lagged behind to other protein modifications. In this study, we describe the re-analysis of global proteomic data from breast cancer xenograft tissues using recently developed software package GPQuest 2.0, revealing a large number of previously unidentifiedN-linked glycopeptides. More importantly, we found that using immobilized metal affinity chromatography (IMAC) technology for the enrichment of phosphopeptides had co-enriched a substantial number of sialoglycopeptides, allowing for a large-scale analysis of sialoglycopeptides in conjunction with the analysis of phosphopeptides. Collectively, combined MS/MS analyses of global proteomic and phosphoproteomic datasets resulted in the identification of 6,724 N-linked glycopeptides from 617 glycoproteins derived from two breast cancer xenograft tissues. Next, we utilized GPQuest for the re-analysis of global and phosphoproteomic data generated from 108 human breast cancer tissues that were previously analyzed by Clinical Proteomic Analysis Consortium (CPTAC). Reanalysis of the CPTAC dataset resulted in the identification of 2,683 glycopeptides from the global proteomic data set and 4,554 glycopeptides from phosphoproteomic data set, respectively. Together, 11,292 N-linked glycopeptides corresponding to 1,731 N-linked glycosites from 883 human glycoproteins were identified from the two data sets. This analysis revealed an extensive number of glycopeptides hidden in the global and enriched in IMAC-based phosphopeptide-enriched proteomic data, information which would have remained unknown from the original study otherwise. The reanalysis described herein can be readily applied to identify glycopeptides from already existing data sets, providing insight into many important facets of protein glycosylation in different biological, physiological, and pathological processes.

scholarly journals Machine learning for discovering missing or wrong protein function annotations

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Felipe Kenji Nakano ◽  
Mathias Lietaert ◽  
Celine Vens
Keyword(s):  
Machine Learning ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Daily Basis ◽  
Proteomic Data ◽  
Evaluation Task ◽  
Protein Functions ◽  
Benchmark Datasets ◽  
Or Gene ◽  
New Protein

Abstract Background A massive amount of proteomic data is generated on a daily basis, nonetheless annotating all sequences is costly and often unfeasible. As a countermeasure, machine learning methods have been used to automatically annotate new protein functions. More specifically, many studies have investigated hierarchical multi-label classification (HMC) methods to predict annotations, using the Functional Catalogue (FunCat) or Gene Ontology (GO) label hierarchies. Most of these studies employed benchmark datasets created more than a decade ago, and thus train their models on outdated information. In this work, we provide an updated version of these datasets. By querying recent versions of FunCat and GO yeast annotations, we provide 24 new datasets in total. We compare four HMC methods, providing baseline results for the new datasets. Furthermore, we also evaluate whether the predictive models are able to discover new or wrong annotations, by training them on the old data and evaluating their results against the most recent information. Results The results demonstrated that the method based on predictive clustering trees, Clus-Ensemble, proposed in 2008, achieved superior results compared to more recent methods on the standard evaluation task. For the discovery of new knowledge, Clus-Ensemble performed better when discovering new annotations in the FunCat taxonomy, whereas hierarchical multi-label classification with genetic algorithm (HMC-GA), a method based on genetic algorithms, was overall superior when detecting annotations that were removed. In the GO datasets, Clus-Ensemble once again had the upper hand when discovering new annotations, HMC-GA performed better for detecting removed annotations. However, in this evaluation, there were less significant differences among the methods. Conclusions The experiments have showed that protein function prediction is a very challenging task which should be further investigated. We believe that the baseline results associated with the updated datasets provided in this work should be considered as guidelines for future studies, nonetheless the old versions of the datasets should not be disregarded since other tasks in machine learning could benefit from them.

BIOCONTROL OF PLANT DISEASES: PRODUCTION AND TESTING OF NOVEL ELICITORS INDUCING PLANT DEFENSE RESPONSE

2009 ◽  
pp. 363-366
Author(s):  
J. Soliveri ◽  
M. Arenas ◽  
J.L. Copa-Patiño ◽  
J.L. Caballero ◽  
J. Muñoz-Blanco
Keyword(s):  
Plant Defense ◽  
Defense Response ◽  
Plant Diseases ◽  
Plant Defense Response

Root exposure to apple replant disease soil triggers local defense response and rhizoplane microbiome dysbiosis

2021 ◽  
Author(s):  
Alicia Balbín-Suárez ◽  
Samuel Jacquiod ◽  
Annmarie-Deetja Rohr ◽  
Benye Liu ◽  
Henryk Flachowsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Plant Defense ◽  
Defense Response ◽  
Soil Column ◽  
Soil Layer ◽  
Soil Microbiome ◽  
Control Soil ◽  
Apple Rootstocks ◽  
Apple Replant Disease ◽  
Replant Disease

Abstract A soil column split-root experiment was designed to investigate the ability of apple replant disease (ARD) causing agents to spread in soil. ‘M26’ apple rootstocks grew into a top layer of Control soil, followed by a barrier-free split-soil layer (Control soil/ARD soil). We observed a severely reduced root growth, concomitant with enhanced gene expression of phytoalexin biosynthetic genes and phytoalexin content in roots from ARD soil, indicating a pronounced local plant defense response. Amplicon sequencing (bacteria, archaea, fungi) revealed local shifts in diversity and composition of microorganisms in the rhizoplane of roots from ARD soil. An enrichment of OTUs affiliated to potential ARD fungal pathogens (Ilyonectria and Nectria sp.) and bacteria frequently associated with ARD (Streptomyces, Variovorax) was noted. In conclusion, our integrated study supports the idea of ARD being local and not spreading into surrounding soil, as only the roots in ARD soil were affected in terms of growth, phytoalexin biosynthetic gene expression, phytoalexin production, and altered microbiome structure. This study further reinforces the microbiological nature of ARD, being likely triggered by a disturbed soil microbiome enriched with low mobility ARD-causing agents that induce a strong plant defense and rhizoplane microbiome dysbiosis, concurring with root damage.

scholarly journals AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3830
Author(s):  
Ahmad Almadhor ◽  
Hafiz Tayyab Rauf ◽  
Muhammad Ikram Ullah Lali ◽  
Robertas Damaševičius ◽  
Bader Alouffi ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Color Difference ◽  
Plant Diseases ◽  
Economic Losses ◽  
Production Loss ◽  
Postharvest Diseases ◽  
Tropical Regions ◽  
Guava Fruit ◽  
Tree Classifier

Plant diseases can cause a considerable reduction in the quality and number of agricultural products. Guava, well known to be the tropics’ apple, is one significant fruit cultivated in tropical regions. It is attacked by 177 pathogens, including 167 fungal and others such as bacterial, algal, and nematodes. In addition, postharvest diseases may cause crucial production loss. Due to minor variations in various guava disease symptoms, an expert opinion is required for disease analysis. Improper diagnosis may cause economic losses to farmers’ improper use of pesticides. Automatic detection of diseases in plants once they emerge on the plants’ leaves and fruit is required to maintain high crop fields. In this paper, an artificial intelligence (AI) driven framework is presented to detect and classify the most common guava plant diseases. The proposed framework employs the ΔE color difference image segmentation to segregate the areas infected by the disease. Furthermore, color (RGB, HSV) histogram and textural (LBP) features are applied to extract rich, informative feature vectors. The combination of color and textural features are used to identify and attain similar outcomes compared to individual channels, while disease recognition is performed by employing advanced machine-learning classifiers (Fine KNN, Complex Tree, Boosted Tree, Bagged Tree, Cubic SVM). The proposed framework is evaluated on a high-resolution (18 MP) image dataset of guava leaves and fruit. The best recognition results were obtained by Bagged Tree classifier on a set of RGB, HSV, and LBP features (99% accuracy in recognizing four guava fruit diseases (Canker, Mummification, Dot, and Rust) against healthy fruit). The proposed framework may help the farmers to avoid possible production loss by taking early precautions.

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